Camera calibration defines the relationship between an image and the scene that the image depicts. Calibration characterizes the photometric and geometric properties of the camera, that define, respectively how pixels of the camera report color and intensity of the scene, and where scene elements appear on the image.
Most common calibration approaches start with an image of an object with known 3-D geometry, or several images of an object with known 2-D geometry, find correspondences between points on the object and points in the image, and use these to solve for the camera geometry.
There are some approaches that are not based on identifying exact corresponding points in a scene. Calibration patterns that consist of patches of parallel lines can be used for intrinsic camera calibration and extrinsic calibration (including rotation and translation relative to other cameras). Approaches that do not require correspondence between specific lines are based on seeing the orientation and spacing of those parallel lines on the image and include those based on the prismatic line constraint, and an approach using the Radon transform as a filtering operator. In some circumstances, there are scenes that have large numbers of parallel lines with a known orientation, such as vertical edges of buildings or plumb-lines; the orientation and position of those lines in an image provide constraints for both intrinsic and extrinsic calibration even without matching the pixel to a specific line in the world.
Other approaches use textures for calibration which assumes that natural urban textures have low rank (for example, grids of windows on the exterior of a building). Using this assumption, calibration and lens distortion that minimizes the rank of the textures in the image, using all pixels, not just point locations of geometric points is solved for.